Stress Testing: The Post-crisis Elixir of Regulators 82 nd International Atlantic Economic Conference, Washington DC October 14, 2016 Til Schuermann FINANCIAL SERVICES Oliver Wyman
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Hindsight is a wonderful thing David Beckham
Hindsight bias makes surprises vanish Daniel Kahneman (2002 Nobel Prize winner in economics; author of Thinking Fast, Thinking Slow)
My big take-away from the crisis: it s really, really, really hard, ex ante, to predict what will, let alone what might happen The risk manager s perennial problem A consequence of the efficient market hypothesis, and a recognition that the market is the best information aggregator we have Can t systematically predict returns (on average, alpha = 0) but some people are better informed than others Oddly, volatilities are (somewhat) predictable E.g. GARCH models But that is not helpful for predicting market disruptions These ideas have several Nobel prizes behind them It is hard to predict tail outcomes It is really hard to predict far tails It is nearly impossible to predict disruptions And when one does happen, it is really hard to know if it s short or long duration However, it doesn t seem to stop us from feeling confident about designing stress scenarios
Risk managers would frequently look towards historical precedents as an indicator for the worst case scenario stress event TED spread bps, Jan 1990-June 2006 VIX Points, Jan 1990-June 2006 200 180 160 140 120 100 80 60 40 20 0 50 45 40 35 30 25 20 15 10 5 0 Oliver Wyman
Risk managers would frequently look towards historical precedents as an indicator for the worst case scenario stress event TED spread bps, Jan 1990-June 2006 500 450 400 350 300 250 200 150 100 50 0 VIX Points, Jan 1990-June 2006 100 90 80 70 60 50 40 30 20 10 0 Oliver Wyman
but during the crisis many of these metrics reached unprecedented levels TED spread bps, Jan 1990-Feb 2015 VIX Points, Jan 1990-June 2015 500 450 400 Oct. 10, 2008 100 90 80 Oct. 27, 2008 350 70 300 60 250 50 200 40 150 30 100 20 50 10 0 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Oliver Wyman
All hope is not lost. Creatively designed stress test scenarios allows the regulated and the regulators to probe the tails Regulators Choice of scenario should expose and probe the vulnerabilities of the financial system Of banks and other financial institutions Requires close collaboration between economists and banking supervisors Scenario can only expose vulnerability of the average bank Not all banks are vulnerable to the same scenario Oliver Wyman Banks (and other FIs) Choice of scenario should probe the vulnerabilities of the firm Type of business Products sold Clients served Geographies covered Combination of systematic risk factor exposure (housing? FX? oil?) and idiosyncratic risk (cyber; earthquake; robo advisors) Tied to strategic plan Forces confrontation of firm s optimists with the possibility of bad outcomes
Stress testing has become a big deal in banking, on both sides of the Atlantic Currently, 33 US bank holding companies participate in the Fed s CCAR program They represent about 80% of domestic US banking system assets Together they have $14.3 TN in assets supported by $1.2 TN in common equity Tier 1 Total assets held by all publicly traded non-financial firms in the US is $16.7 TN* Banks submit several 100,000s data items and 10,000s pages of support documentation Post-stress, these banks had more capital than all US banks had YE 2006: $886 BN vs. $743 BN The EBA and ECB ran stress testing exercises for significant institutions in 2016 Coverage >70% of total EU banking assets Banks with total assets > 30 BN 53 banking groups of which 37 supervised by the SSM SSM** stress tested (privately) another 56 banks not in EBA exercise In 2014, ECB/SSM conducted a Comprehensive Assessment (AQR & ST) of 130 banking groups from 19 countries, covering over 80% of total banking assets in euro-land ( 22 TN) * Source: SNL; all numbers YE2015 ** SSM: Single Supervisory Mechanism
Macro-prudential stress testing started as a crisis response tool and has evolved to a peacetime tool for bank oversight Stress testing as a crisis response tool Stress test is deployed as a one-time response to a specific crisis (e.g. SCAP) Main purpose is to provide assurance to the markets by Credibly (and conservatively) sizing the potential impact of a crisis Providing evidence that a bank has sufficient capital to withstand crisis Given purpose, quantitative output of stress test is most important (i.e. does a bank have enough capital to withstand the stress) Stress testing as an ongoing risk management tool Regulatory stress test is a regular occurrence Broader purpose Assessment of capital adequacy (quantitative) Assessment of an institution s risk identification, measurement, management and governance capabilities (qualitative) Leverages perhaps the only informational advantage of the regulator: Ability to compare horizontally Useful for quantitative and qualitative If wartime is about getting capital into banks, peacetime is about deciding whether to let it out Oliver Wyman
Stress tests in U.S. and Europe CEBS 10 EBA 11, 14, 16 ~24% GDP World Capital shortfall: ~ 3.5BN (2010) ~ 2.5BN (2011) ~ 24.6BN (2014) ~ 5.7BN (2016)* USA 09 2011-16 ~24% GDP World Capital shortfall (2009): ~$75BN * No pass/fail threshold. Monte Paschi projected min CET1 was -2.4%
USA (CCAR & DFAST) ~24% GDP World ~90% banking assets Stress tests around the world in 2016 UK ~3.9% GDP World ~ 80% PRA regulated lending EBA/SSM ~24% GDP World Mexico ~1.6% GDP World ~70% banking assets ~ 95% banking assets Brazil ~2.4% GDP World ~ 90% banking assets
All stress tests share a common feature: take a scenario, map to outcomes Stress testing separates (somewhat) systematic from idiosyncratic risk The basic approach to stress testing is the same on both sides of the Atlantic (and elsewhere) 1. Design a scenario Which risk factors? How harsh? Harsh for who? 2. Translate to outcomes Losses: loans, securities, trading position Net revenues: net interest income, non-interest income, non-interest expense capital impact How to translate or map the scenario to the outcomes? Who? Whose models? A big difference between left and right side of Atlantic approach has been the use of models to generate projections Heavy on left, light on right (but getting heavier)
Real GDP growth (Y-on-Y; %) Dow Jones Total Stock Market Index 8.0 25,000 6.0 4.0 20,000 2.0 15,000 0.0-2.0 10,000-4.0-6.0 5,000-8.0 0-10.0 Federal Reserve Stress Scenarios Historical actuals SCAP Historical actuals CCAR-2011 CCAR-2011 CCAR-2012 CCAR-2012 CCAR-2013 CCAR-2013 CCAR-2014 CCAR-2014 CCAR-2015 CCAR-2015 CCAR-2016 CCAR-2016
Bank of England Stress Scenarios Real GDP growth (Q-on-Q; %) 2.0 1.0 0.0 2010 2012 2014 2016 2018 2020-1.0-2.0 FTSE All Share Index 120 100 80 60 40 20-3.0 Historical actuals 2014 Stress Test 2015 Stress Test 2016 Stress Test 0 2010 2012 2014 2016 2018 2020 Historical actuals 2014 Stress Test 2015 Stress Test 2016 Stress Test
The Fed has gone furthest in terms of building internal capability to generate projections of bank financials via supervisory models Fed s modeling capabilities allow for projection of full financials All losses, all revenues, costs, funding, balance sheet, RWA Incorporation of planned capital actions: dividend increases, share repurchases, capital stack restructuring Using quarterly (and for some consumer products like mortgages, monthly) data feeds from banks, often loan level Starting balance sheet Q1 income statement Q1 end balance sheet A L P&L A L E E Capital ratios Capital ratios This is very powerful, but very resource intensive! Also allows for robust challenge by Fed of models used by banks
What should we be worrying about with stress testing in peacetime? (1/2) Scenario design Hard to balance coherence (reliance on past joint distribution of risk factors) with imagination (stuff breaks down) Run many different scenarios Different and less (or more) harsh are not the same thing Force banks to design several of their own in ways that probe on their vulnerabilities (already the case in US CCAR program) More broadly, a narrow gene pool of ideas and tools On scenarios: nearly all (all?) are a variation on the crisis and last recession On translation to outcomes: we re all looking at the same data (banks and supervisors alike), and it s hard to resist the temptation to make models more accurate instead of robust Small gene pool population (system) very vulnerable to the next financial virus
What should we be worrying about with stress testing in peacetime? (2/2) Opacity of stress tests Stress tests are often opaque, reducing the regulators accountability The same opacity makes it difficult for outside experts to catch problems or point out trade-offs Stress tests re-nationalize capital regulation that we d been working hard to make global May well create problems with geographical regulatory arbitrage
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